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Craig Schwartz and Zhiquan Liu NCAR/NESL/MMM schwartz@ucar

Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, EnKF and “hybrid” data assimilation systems. Craig Schwartz and Zhiquan Liu NCAR/NESL/MMM schwartz@ucar.edu NCAR is sponsored by the National Science Foundation. Introduction.

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Craig Schwartz and Zhiquan Liu NCAR/NESL/MMM schwartz@ucar

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  1. Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, EnKF and “hybrid”data assimilation systems Craig Schwartz and Zhiquan Liu NCAR/NESL/MMM schwartz@ucar.edu NCAR is sponsored by the National Science Foundation

  2. Introduction • Convection-permitting forecasts have commonly been initialized from operational analyses (e.g., GFS, NAM) • Example: Interpolate GFS analysis onto WRF domain • Continuously cycling mesoscale data assimilation systems can produce initial conditions for convection-permitting forecasts • Dynamically consistent analysis/forecast system

  3. A few data assimilation approaches • Three-dimensional variational (3DVAR) • Background error covariances (BECs) typically fixed/time-invariant • May yield poor results when actual flow differs from that encapsulated within the fixed “climatology” • Ensemble Kalman filter (EnKF) • Time-evolving, “flow-dependent” BECs estimated from a background ensemble

  4. A few data assimilation approaches • “Hybrid” variational/ensemble • Incorporates ensemble background errors within a variational framework • Combination of fixed and time-evolving background errors 75% squirrel 25% cat

  5. Experimental design • Full-cycling (6-hr period) between May 6 – June 21, 2011 • Data assimilation/cycling on a 20-km domain • Three experiments assimilating identical observations: • Pure 3DVAR • Pure EnKF • Hybrid • 0000 UTC analyses initialized 36-hr 4-km forecasts • EnKF: 4-km forecasts initialized from mean analyses • Control: Interpolate 0000 UTC GFS analyses directly onto the domain and run forecasts • GFS initialized from 3DVAR analyses in 2011

  6. Cycling data assimilation: Hybrid/EnKF flowchart

  7. Computational domain

  8. WRF settings and physics • Forecast model: WRF-ARW (version 3.3.1) • 57 vertical levels, 10 hPa top • Physics: • Morrison double-moment microphysics • RRTMG longwave and shortwave radiation • MYJ PBL • Tiedtke cumulus parameterization (20-km domain only) • NOAH land surface model • Aerosol, ozone climatologies for RRTMG

  9. Selected data assimilation settings • NCEP’s Gridpoint Statistical Interpolation (GSI) data assimilation system: • GSI-3DVAR • GSI-hybrid • Ensemble square root Kalman filter (EnSRF) • 50 ensemble members • Hybrid: 75% of the background errors from the ensemble, 25% from the static contribution • Used posterior inflation for EnSRF and localization in both EnSRF and hybrid

  10. Observation snapshot (0000 UTC 25 May)

  11. Precipitation verification • Focus on 4-km precipitation forecasts • NCEP Stage IV observations as “truth” • Verified hourly precipitation forecasts • All precipitation statistics shown are aggregated over 444-km forecasts • Fractions skill score (FSS) quantifies displacement errors

  12. Precipitation Bias Aggregated hourly over 18-36-hr forecasts Aggregated hourly over the first 12 forecast hrs

  13. FSS: The first 12-hrs 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr

  14. FSS: Forecast hours 18-36 0.25 mm/hr 1.0 mm/hr 10.0 mm/hr 5.0 mm/hr

  15. For more information… • All of the previous material was summarized in this publication: Schwartz, C. S., and Z. Liu, 2014: Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, ensemble Kalman filter, and “hybrid” variational-ensemble data assimilation systems. Mon. Wea. Rev., 142, 716–738, doi: 10.1175/MWR-D-13-00100.1.

  16. Preview of new work • Recently, the exact same experiments were performed but over a new period: • May 4 – June 30, 2013 • 55 4-km forecasts • Near identical configuration as before, except used Thompson microphysics • Also performed dual-resolution hybrid analyses with a 4-km deterministic background and 20-km ensemble

  17. Cycling data assimilation: Hybrid/EnKF flowchart 20-km 4-km

  18. FSS: The first 12-hrs 2013 experiments: FSS aggregated over 55 forecasts 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr

  19. FSS: The first 12-hrs 2013 experiments: FSS aggregated over 55 forecasts Dual-resolution hybrid: 4-km analyses and subsequent forecasts 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr

  20. FSS: Forecast hours 18-36 2013 experiments: FSS aggregated over 55 forecasts 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr

  21. Summary • Precipitation bias characteristics similar in the cycling experiments • Differences in precipitation placement evident • Hybrid and EnSRF performed best • Shows the benefit of flow-dependent background errors • Further improvement possible with high-resolution analyses

  22. Example forecast 6-hr forecast initialized 0000 UTC 24 May 2011

  23. Example forecast 30-hr forecast initialized 0000 UTC 24 May 2011

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